Acute respiratory distress syndrome (ARDS) is a common and often fatal condition for which there is no effective treatment other than supportive care centered on mechanical ventilation. Mechanical ventilation itself, however, can easily cause damage to already injured lung tissues, leading to ventilator-induced lung injury (VILI). The principle goal in managing ARDS is thus to administer mechanical ventilation in a manner that avoids, or at least minimizes, VILI. The standard of care in ARDS involves use of small tidal volumes (Vt), the current ideal being 6 ml/kg ideal body weight, together with positive end-expiration pressure (PEEP) to prevent lung collapse and improve oxygenation. These strategies have led to improved outcomes, but ARDS mortality remains high, so better approaches to mechanically ventilating the injured lung are desperately needed. Unfortunately, continuing to search for one-size-fits-all approaches to mechanical ventilation of the very heterogeneous ARDS patient population is rapidly becoming futile because of the huge number of patients that would be needed to obtain statistically significant improvements over current strategies. For this reason, the search for improved approaches to mechanical ventilation in ARDS must focus on strategies that can be tailored to suit the pathophysiological characteristics of individual patiens. Furthermore, such strategies must be adaptable to the evolving nature and severity of ARDS as it runs its course. These considerations lead us to propose that personalized mechanical ventilation of the ARDS patient must take place within an ongoing feedback loop involving three interdependent processes: 1) assessing the injury status of a given lung, 2) predicting how much VILI will be caused in that lung by a given regimen of mechanical ventilation, and 3) optimizing ventilation to be minimally injurious based on the information provided in steps 1 and 2. This will allow the imposed regimen of mechanical ventilation to be responsive to the ventilatory needs of the patient, while at the same time minimizing the amount of VILI that is produced so that the patient's own reparative processes have the best chances of prevailing. We have undertaken extensive prior studies that show we can assess the current state of injury of the lung most effectively by measuring how its mechanical properties change over time as a result of ongoing recruitment and derecruitment. We have also developed computational models showing how it is, in principle, possible to predict the amount of VILI that will be produced by a given regimen of mechanical ventilation. Our overarching goal in this proposal is to leverage these findings to optimize the personalized design of mechanical ventilation strategies for the injured lung. This goal will be pursued experimentally in mouse models of ARDS and VILI, and computationally by fitting the data obtained to computational models of lung mechanics and VILI development.